We created an AI support for diagnosis in dyspneic adults at time of triage in the emergency department.

Complete data from an entire regional health care system was analyzed, to find AI-derived, unknown, important diagnostic predictors. Most important were prior diagnoses of heart failure or COPD, daily smoking, atrial fibrillation/flutter, life difficulties and maternal care.

Sensitivity for AHF, eCOPD and pneumonia was 75%, 93%, and 54%, respectively, with a specificity above 75%.

Each patient visit received an individual graph with the AI´s underlying decision basis.
StatusPublished - 2023 sep.
EvenemangEuropean Emergency Medicine Congress 2023 - Barcelona, Spanien
Varaktighet: 2023 sep. 172023 sep. 20


KonferensEuropean Emergency Medicine Congress 2023

Ämnesklassifikation (UKÄ)

  • Kardiologi

Fria nyckelord

  • Artificiell intelligens
  • AI
  • Dyspne


Utforska forskningsämnen för ”Design of an AI Support for Diagnosis of Dyspneic Adults at Time of Triage in the Emergency Department”. Tillsammans bildar de ett unikt fingeravtryck.

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